DRIVING BEHAVIOR UTILIZING WIFI SIGNAL PERCEPTION

This study focuses on utilizing WIFI signal perception technology to monitor driving behaviors, aiming to provide an innovative and efficient solution for intelligent transportation systems. By delving into the principles, characteristics, and operational steps of WIFI perception technology, and integrating deep learning algorithms to construct a model for identifying dangerous driving behaviors, extensive experimental validations were conducted. The research demonstrates that this method can accurately identify obvious behaviors such as sudden acceleration and hard braking, as well as subtle behaviors like distracted and fatigued driving under certain conditions. This approach not only achieves non-invasive, all-weather, and privacy-friendly driving behavior recognition but also provides real-time warning support in complex environments, significantly reducing traffic accident rates caused by dangerous driving. Moreover, this study pioneers the integration of the fine-grained characteristics of WIFI signals with spatiotemporal deep networks, overcoming the limitations of traditional monitoring technologies and injecting new vitality into the field of intelligent transportation. It also offers significant references for further optimizing driving behavior monitoring.